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bear-query

Query the Bear EHR database using the semantic layer. Use when asked about clients, billing, appointments, insurance, clinical data, procedures, scheduling, programs, staff, vitals, diagnoses, revenue, claims, or any data question about the Bear application.

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Bear EHR Database Query Skill

You have access to the Bear EHR MariaDB database via MCP tools (mcp__bear-db__execute_query, mcp__bear-db__get_table_info, mcp__bear-db__get_schema).

Before Writing Any Query

  1. Read the semantic layer docs in /home/andres/code/claude/semantic/ to understand the schema
  2. Start with 00_query_guide.md for query patterns, naming conventions, and caveats
  3. Read the relevant domain file (01-15) for the specific tables involved

Domain Files Quick Reference

#FileUse When Asking About
0000_query_guide.mdNL-to-SQL patterns, status derivation, caveats
0101_people_demographics.mdClients, demographics, addresses, phones
0202_programs_enrollment.mdPrograms, enrollment, locations
0303_insurance_coverage.mdInsurance policies, payors, plans, authorizations
0404_appointments_scheduling.mdScheduling, calendar, appointment types
0505_encounters_forms.mdClinical documentation, encounter forms
0606_billing_revenue.mdCharges, claims, payments, revenue cycle
0707_clinical_data.mdDiagnoses, medications, vitals, labs
0808_users_staff.mdStaff, providers, credentials, roles
0909_tfc.mdTreatment Foster Care
1010_otp.mdOpioid Treatment Program
1111_transportation.mdClient transportation
1212_crew_groups.mdGroup therapy sessions
1313_bed_management.mdResidential bed tracking
1414_prescriptions_erx.mdElectronic prescribing
1515_labs.mdLaboratory orders and results

Critical Rules

MariaDB Syntax (NOT PostgreSQL)

  • Use CASE WHEN not FILTER(WHERE)
  • Use CURDATE() not CURRENT_DATE
  • Use TIMESTAMPDIFF() for date math
  • Use DATE_FORMAT() for date formatting
  • tinyint(1) is boolean — use = 1 / = 0, NOT IS TRUE / IS FALSE

Soft Deletes

  • Most tables use deleted_at IS NULL to filter active records
  • Always include this filter unless the user wants historical data
  • Some tables (like cssrs_suicides, suicidal_ideations, client_high_risks) do NOT have deleted_at — check with get_table_info first if unsure

Names Are in a Separate Table

  • Client names: JOIN names n ON n.person_id = p.id AND n.deleted_at IS NULL
  • Staff names: JOIN people p ON p.identifiable_id = u.id AND p.identifiable_type = 'User' then JOIN names n ON n.person_id = p.id
  • For latest name: use MAX(n.id) subquery or ORDER BY n.id DESC LIMIT 1

STI Tables

  • people.type = 'Client' for patients
  • companies.type = 'Payor' for insurance companies
  • Hidden/merged clients: filter with (p.hidden IS NULL OR p.hidden = 0)

Money Columns

  • ALL financial amounts are *_cents columns (integers)
  • Always divide by 100.0 for dollar display: amount_cents / 100.0 AS amount_dollars

Appointment Status (Timestamp-Based)

  • No status column — derive from timestamps:
    • Completed: checked_out_at IS NOT NULL
    • No-show: noshow_at IS NOT NULL
    • Cancelled: cancelled_at IS NOT NULL
    • Checked in: checked_in_at IS NOT NULL AND checked_out_at IS NULL
    • Valid: deleted_by IS NULL AND cancelled_at IS NULL AND noshow_at IS NULL

Insurance

  • Self-pay payor: companies.id = 1 (scope self_pay / not_self_pay in Rails)
  • Priority values: first, second, third, fourth, fifth, self
  • Cards (images): pictures table with picturable_type = 'InsurancePolicy'

Vitals

  • BP columns: bp_right_arm_sys, bp_right_arm_dia, bp_left_arm_sys, bp_left_artm_dia (note typo on left arm diastolic)
  • High BP threshold: systolic >= 130 OR diastolic >= 80

Suicide Risk (C-SSRS)

  • Main table: cssrs_suicides (no deleted_at column)
  • Ideations: suicidal_ideations joined via cssrs_suicide_id
  • Risk assessments: risk_assessments (has deleted_at)

Query Workflow

  1. Read the relevant semantic layer file(s)
  2. If unsure about a column, use mcp__bear-db__get_table_info to verify
  3. Write the query using MariaDB syntax
  4. Present results in a clear table format
  5. Highlight key insights and anomalies
  6. When results are large, get summary counts first, then drill down

Responding

  • Always show the data in a formatted markdown table
  • Add key takeaways / insights after the data
  • Flag data quality issues (nulls, suspicious values, inconsistencies)
  • Offer to drill deeper or pivot the analysis
  • When providing ActiveRecord equivalents, give them as one-liners for easy console paste

$ARGUMENTS

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